Global coccolithophore diversity: Drivers and future change

Progress in Oceanography 140 (2016) 27–42

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Global coccolithophore diversity: Drivers and future change Colleen J. O’Brien ⇑, Meike Vogt, Nicolas Gruber Environmental Physics, Institute for Biogeochemistry and Pollutant Dynamics, ETH Zurich, Universitätstrasse 16, 8092 Zurich, Switzerland

a r t i c l e

i n f o

Article history: Received 16 March 2015 Received in revised form 11 September 2015 Accepted 15 October 2015 Available online 20 October 2015

a b s t r a c t We use the MAREDAT global compilation of coccolithophore species distribution and combine them with observations of climatological environmental conditions to determine the global-scale distribution of coccolithophore species diversity, its underlying drivers, and potential future changes. To this end, we developed a feed-forward neural network, which predicts 78% of the observed variance in coccolithophore diversity from environmental input variables (temperature, PAR, nitrate, silicic acid, mixed layer depth, excess phosphate (P⁄) and chlorophyll). Light and temperature are the strongest predictors of coccolithophore diversity. Coccolithophore diversity is highest in the low latitudes, where coccolithophores are a relatively dominant component of the total phytoplankton community. Particularly high diversity is predicted in the western equatorial Pacific and the southern Indian Ocean, with additional peaks at approximately 30°N and 30°S. The global, zonal mean pattern is dominated by the Pacific Ocean, which shows a clear latitudinal gradient with diversity peaking at the equator, whereas in the Atlantic Ocean diversity is highest in the subtropics. We find a unimodal relationship between coccolithophore diversity and biomass, as has previously been observed for total phytoplankton assemblages. In contrast, diversity shows a negative relationship with total chlorophyll. Applying our diversity model to projections from the CMIP5 climate models, we project an increase in the diversity of coccolithophore assemblages by the end of this century. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction Biological diversity has been linked to key ecosystem characteristics such as resilience and productivity (Worm and Duffy, 2003; Folke et al., 2004; Ptacnik et al., 2008), as well as to many other aspects of ecosystem functioning (Worm et al., 2006; Bracken et al., 2008; Behl et al., 2011; Cardinale et al., 2011). With significant changes in marine species distributions likely already occurring due to the interacting effects of climate change, ocean acidification and other anthropogenic pressures (Beaugrand et al., 2002, 2010; Reid et al., 2007), it is increasingly important to understand the relationship between diversity and ecosystem structure and function as well as the potential impact of environmental change on the provision of essential ecosystem services. Studies of terrestrial diversity have shown a relatively consistent pattern of highest diversity in the tropics, declining toward the poles (Rosenzweig, 1992; Willig et al., 2003). Several theories have been developed to explain diversity patterns in terrestrial systems. Firstly, diversity has been linked to energy availability (i.e. photosynthetically active radiation (PAR) in the case of primary producers). Energy input can affect diversity either ⇑ Corresponding author. E-mail address: [email protected] (C.J. O’Brien). http://dx.doi.org/10.1016/j.pocean.2015.10.003 0079-6611/Ó 2015 Elsevier Ltd. All rights reserved.

through increased speciation rates at warmer temperatures, or indirectly through the effect of energy availability on abundance and biomass (Clarke and Gaston, 2006). Alternatively, the latitudinal diversity gradient may be related to factors such as the temporal stability of the tropics or the random effects of overlapping ranges (Colwell and Hurtt, 1994; Rosenzweig, 1992). In contrast to terrestrial ecosystems, diversity patterns in marine ecosystems appear to be less consistent across taxa and remain relatively poorly understood. Whereas many early studies suggested that diversity of marine taxa was highest in the tropics, more recent observational studies have shown that many pelagic taxa, including predatory fish species (Worm et al., 2005), foraminifera (Rutherford et al., 1999) and tintinnids (Dolan et al., 2006) show patterns of highest diversity in the subtropics. Phytoplankton assemblages show a similar pattern of subtropical diversity peaks in the Atlantic Ocean (Cermeño et al., 2008). In contrast, a recent modelling study (Barton et al., 2010) predicted highest phytoplankton diversity at the equator, consistent with observations for marine bacterioplankton (Pommier et al., 2007; Fuhrman et al., 2008). However, more recent studies based on the same model but with a different grazing parameterisation (Prowe et al., 2012; Vallina et al., 2014a) came to rather different conclusions, suggesting that phytoplankton diversity patterns may be highly sensitive to top-down pressure. It therefore remains

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unclear whether latitudinal diversity patterns are as consistent and ubiquitous in the ocean as they are on land. There is mixed evidence as to whether diversity patterns identified within a single taxonomic group or trophic level can be extrapolated to other groups, as would be expected if diversity were simply a function of energy availability. While there appears to be a positive correlation between zooplankton and predator diversity (Worm et al., 2005), no correlation has been found between phytoplankton and zooplankton diversity (Irigoien et al., 2004). It is therefore uncertain whether diversity hotspots identified for a single taxonomic group or trophic level can also be considered hotspots for the marine community as a whole. Similarly, the relationship between diversity and productivity and its robustness to environmental change remains unclear. Terrestrial and aquatic studies have variously reported unimodal, linear (both positive and negative) and saturating relationships between diversity and productivity (Waide et al., 1999; Cardinale et al., 2011). A unimodal pattern has been observed for marine phytoplankton and zooplankton (Irigoien et al., 2004; RodríguezRamos et al., 2015), and reproduced for marine phytoplankton in a global ecosystem model (Vallina et al., 2014a), though Cermeño et al. (2013) claimed that this relationship may simply be an artefact of incomplete sampling of species in low productivity regions, and Rodríguez-Ramos et al. (2015) found biomass to be a poor predictor of phytoplankton diversity. Coccolithophores are a group of calcifying marine nanoplankton that have received considerable research attention in recent years due to their potential sensitivity to ocean acidification (Riebesell and Zondervan, 2000; Iglesias-Rodriguez et al., 2008; Beaufort et al., 2011). Coccolithophores form large blooms in temperate ocean regions and are thought to be a major contributor to global calcite production (Balch et al., 2007). While many studies have focused on coccolithophore bloom occurrence (Iglesias-Rodríguez et al., 2002; Tyrrell and Merico, 2004) or growth and calcification rates (e.g. Iglesias-Rodriguez et al., 2008; Müller et al., 2010; Lohbeck et al., 2012; Schlüter et al., 2014), relatively little is known of the global-scale patterns or environmental controls of coccolithophore diversity. Given the strong links identified between diversity and ecosystem functioning for other taxa (e.g. Worm et al., 2006; Reich et al., 2012; Cardinale et al., 2011), the investigation of coccolithophore diversity patterns and their controls is therefore an important step in understanding how coccolithophore assemblages may be affected by climate change and ocean acidification. Based on species lists of coccolithophores identified in different ocean regions, it has previously been suggested that coccolithophore diversity is highest in the tropics and/or the subtropical gyres, with low diversity in temperate and polar waters and coastal regions (Brand, 1994; Winter et al., 1994). Diversity was also suggested to be higher in the Atlantic than in other basins (e.g. Gaarder, 1971). There have been, however, few quantitative investigations of the diversity of coccolithophore assemblages at either the local, regional or global scales. Quantitative diversity analyses based on meridional transects support the hypothesis of highest diversity in the subtropics in both the Atlantic (Böckel and Baumann, 2008; Cermeño et al., 2008) and Pacific Ocean (Honjo and Okada, 1974). We are not aware of comparable large-scale studies of coccolithophore diversity in the Indian Ocean or at the global scale. We use the recently compiled MAREDAT database of global coccolithophore observations (O’Brien et al., 2013) to explore diversity patterns and their likely drivers on the global scale by extrapolating in situ estimates of coccolithophore diversity to the global scale. We do so by using an artificial neural network to predict coccolithophore diversity as a function of expected environmental controls.

Specifically, this paper addresses the following questions: (1) What are the global patterns in coccolithophore diversity? (2) Can coccolithophore diversity be predicted from environmental parameters? (3) What relationship exists between coccolithophore diversity, coccolithophore biomass and total phytoplankton productivity? and (4) How do we expect coccolithophore diversity to be affected by future climate change? 2. Methods We investigated coccolithophore diversity using the MAREDAT database and using a feed-forward artificial neural network to extrapolate these diversity estimates to the global scale. This approach allowed us to investigate the underlying drivers of coccolithophore diversity as well as the likely impacts of future climate change on global coccolithophore diversity patterns. 2.1. Data sources 2.1.1. Coccolithophore data Coccolithophore diversity estimates were calculated using species abundance observations from the MAREDAT coccolithophore database (O’Brien et al., 2013). This database contains observations of coccolithophore abundance and biomass from 6741 stations (i.e. observations from a given geographical location and date, often covering multiple depth levels), with a total of 149 recognised species represented (following the taxonomic scheme of Jordan et al. (2004)). Stations where coccolithophores were not identified to the species level were excluded, resulting in a final set of 3445 stations. Species identification and enumeration was based on light microscopy for 2591 stations, electron microscopy for 113 stations and unspecified methodology for the remaining 741 stations. The inclusion of many historical datasets (sampling year ranging from 1929 to 2008) makes it challenging to accurately determine species identities for many samples in the database, with many obsolete species names and likely misidentifications included. Coccolithophore taxonomy is in a state of ongoing development: for example, many holococcolithophore species have recently been found to be life stages of heterococcolithophore species (Young et al., 2005), and multiple morphotypes have been identified within many species (Paasche, 2002; Quinn et al., 2003, 2005; Young et al., 2005). With increasing use of molecular tools it is highly likely that further changes will occur in coccolithophore taxonomy in the future. Given the taxonomic challenges outlined above, we chose to base our analyses on local or station-level diversity only (‘alpha diversity’; Whittaker et al., 2001), since this does not require us to make assumptions regarding the consistency of species identifications across different datasets. Local diversity has previously been shown to be highly correlated with the diversity of the regional species pool for other marine taxa (Witman et al., 2004; Cornell and Harrison, 2013), and we therefore expect any large-scale diversity patterns that exist to emerge from these station-level diversity estimates. 2.1.2. Environmental data Environmental input variables were chosen based on their expected direct or indirect effect on coccolithophore diversity. As environmental meta-data is unavailable for most observations in the MAREDAT database, we instead substituted them with climatological values. We use monthly, observation-based climatological datasets with global 1
 1
coverage (Table 1), including World Ocean Atlas 2009 (WOA) nutrients (nitrate, phosphate and silicic acid; Garcia et al., 2010) and temperature (Locarnini et al., 2010); SeaWiFS chlorophyll and photosynthetically active radiation (PAR); mixed layer depth (MLD) from de Boyer Montégut

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Table 1 Overview of all input variables considered for inclusion in the model, including data sources, original spatial and temporal resolution of the data and spearman correlation coefficients (r s ) for each variable with respect to coccolithophore diversity. Variable

Source

Original resolution

rs

Reference

Temperature Chlorophyll-a Phosphate P⁄ Nitrate r Nitrate Silicic Acid MLD XCa PAR

WOA09 SeaWiFS WOA09 Calculated from WOA09 WOA09 Calculated from WOA09 WOA09

1°  1°, monthly 9 km, monthly 1°  1°, monthly 1°  1°, monthly 1°  1°, monthly 1°  1°, monthly 1°  1°, monthly 2°  2°, monthly 4°  5°, monthly 9 km, monthly

0.39 0.51 0.44 0.36 0.45 0.38 0.32 0.23 0.31 0.27

Locarnini et al. (2010)

SeaWiFS

(2004, only available at 2° resolution); and calcite saturation state from Takahashi et al. (2014, only available at 4°5° resolution). Additionally, we calculated P⁄ (excess phosphate, based on Redfield N:P ratio; Deutsch et al., 2007), and lateral nitrate gradients from the WOA data. Variables were log-transformed where necessary to improve normality of their distributions (all nutrient fields, chlorophyll and MLD), as this has been shown to improve the function-fitting ability of neural networks (Dransfeld et al., 2006).

2.2. Data treatment 2.2.1. Diversity estimates For each station in the coccolithophore dataset, we calculated the observed species richness between the surface and the bottom of the climatological mixed layer (de Boyer Montégut, 2004). Additionally, we calculated the mean abundance of each species observed within the mixed layer, taken across all sample depths within this depth range. This information was used to standardise our species richness estimates using a process of rarefaction (implemented using the vegan package in R; Oksanen et al., 2012). Rarefaction involves an iterative subsampling of species counts to estimate the likely observed species richness for a given sample size (in this case, 100 cells). This reduces the effect of biases caused by sampling differences and varying dataset completeness by reducing the influence of rare species which are unlikely to have been accurately recorded across all datasets. It also has the advantage of reducing the influence of productivity differences on sampling completeness (Cermeño et al., 2013). Rarefaction is usually carried out using species counts, rather than cell densities as are available in our dataset. Since the original sample volumes and number of cells counted were not available for most observations, we used the reported abundance in cells per litre as the basis for our rarefaction estimates. While we expect this approach to be less robust than true individual-based rarefaction, it nevertheless reduces the effect of variable sampling completeness across the database. An investigation of the sensitivity of our results to the original sample volume revealed that this had a minimal influence on the resulting rarefied species richness estimates. For example, recalculating species richness based on cells per 50 ml (or the minimum volume needed to count 100 cells, if this was greater) rather than cells per litre produced no significant difference in our estimates (paired t-test, p > 0.05). Two additional diversity indices were tested to determine the sensitivity of our model to the choice of diversity index – these were the Shannon-Wiener diversity index, which takes into account both species richness and evenness (Shannon, 1948) and raw species richness without rarefaction. All three diversity indices were found to be highly correlated (Spearman correlation coefficient, r s = 0.80–0.95). Best model performance (lowest root mean squared error, RMSE) was achieved using rarefied species richness

Garcia et al. (2010) Deutsch et al. (2007) Garcia et al. (2010) Garcia et al. (2010) de Boyer Montégut (2004) Takahashi et al. (2014)

as our output variable, and this measure was therefore used for the following analyses. 2.2.2. Data gridding Having calculated species richness estimates for each station, we gridded the data to 1°  1°, monthly median values to match the spatial resolution of our environmental variables and avoid spatial biases from including multiple similar stations from single cruises. Median rather than mean diversity per grid cell was used to reduce the influence of single outlying low or high diversity stations. 2.3. Future projections To investigate how coccolithophore diversity may change due to the effects of climate change over the 21st century, we used projected ocean conditions for the end of the century from four coupled Earth System Models run as part of either CMIP5 (Coupled Model Intercomparison Project; (Taylor et al., 2012)) or MAREMIP (Marine Ecosystem Model Intercomparison Project; see Hashioka et al., 2013): CESM1-BEC (Moore et al., 2002, 2004; Hurrell et al., 2013), IPSL-PISCES (Aumont and Bopp, 2006; Dufresne et al., 2013), CNRM-PISCES (Aumont and Bopp, 2006; Voldoire et al., 2012) and GFDL-TOPAZ (Dunne et al., 2013). All simulations used the RCP8.5 climate scenario – a high emissions scenario with CO2 concentrations reaching 936 ppm and a predicted warming of 2–4 °C by 2100 (Doney et al., 2014). For each model, we calculated anomalies for the variables of interest between the periods 1996–2000 and 2096–2100. The mean model anomaly for each variable was then added to our observation-based climatological fields to generate our future climate scenario for the end of the 21st century in order to reduce potential model biases. 2.4. Productivity–diversity relationship To investigate how coccolithophore diversity may be linked to coccolithophore productivity, total primary productivity or the relative dominance of coccolithophores in phytoplankton assemblages, we explored relationships between the gridded diversity estimates and coccolithophore biomass (from the gridded MAREDAT product), total chlorophyll (from SeaWiFS), and an estimate of coccolithophore dominance. While chlorophyll concentrations are not a direct measurement of primary productivity, a comparison of SeaWiFS-derived chlorophyll and primary production rates (data from Westberry et al., 2008) showed a correlation at the one degree, monthly timescale of 0.39 when all global gridcells were considered, while for the gridcells where we have diversity estimates the correlation was 0.72. For our dataset at least, chlorophyll is therefore a reasonable proxy for primary productivity. Similarly, coccolithophore biomass is expected to be highly correlated with

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coccolithophore productivity. Coccolithophore dominance was defined as the percent contribution of coccolithophores to the expected total chlorophyll concentration for each grid cell, with coccolithophore chlorophyll estimated from the MAREDAT biomass estimates using a carbon:chlorophyll ratio of 65 gg1 (Sathyendranath et al., 2009).

2.5. Model development 2.5.1. Artificial neural network development To estimate global coccolithophore diversity patterns from our sparse gridded data, we used a feed-forward artificial neural network (ANN) to fit a non-linear regression model of coccolithophore diversity as a function of the environmental parameters. Analyses were performed using the MATLAB 2012a neural network toolbox. The network design consisted of an input layer (one neuron for each environmental input variable), a single hidden layer and an output layer consisting of a single neuron (Fig. 1). Multilayer perceptrons of this type have previously been shown to be a powerful tool for fitting non-linear functions (Hornik et al., 1989), and have been successfully applied to a range of ecological questions (e.g. Lek and Guégan, 1999; Scardi, 2001; Woodd-Walker et al., 2001; Özesmi et al., 2006). Activation functions between the input and hidden and hidden and output layers were tan-sigmoidal. Using a sigmoidal activation function for the output layer limits the model output to values within the range of the original diversity values. Training was performed using the Levenberg–Marquardt training algorithm with Bayesian regularisation (MacKay, 1995, 1992). Bayesian regularisation has been shown to be an effective method for avoiding overfitting of neural networks, since it favours the simplest network that will explain the training data through the minimisation of small connection weights. An alternative approach using a validation dataset and early stopping was also tested, but was found to be less robust in preventing overfitting based on a comparison of the generalisation ability of the resulting networks. The generalisation ability of trained networks was quantified using a tenfold cross-validation procedure (Geisser, 1975). This approach involves randomly dividing the dataset into ten equally-sized partitions, with each partition used once as a test dataset while the network is trained using the remaining nine data partitions. In order to avoid the problem of local minima

Chl

I1

SST

I2

P*

I3

N

I4

Si

I5

MLD

I6

PAR

I7

H1

H2 O

DIV

H3

H4

Fig. 1. Neural interpretation diagram (Özesmi et al., 2006) showing the final neural network configuration: 7 input neurons (I1–I7), each corresponding to an environmental input variable; 4 hidden neurons (H1–H4); and a single output neuron (O) corresponding to our species richness estimates (DIV). Lines represent the network connection weights (Table 4). Line thickness is scaled to the magnitude of the connection weights, with solid lines representing positive connection weights and dashed lines representing negative connection weights.

(i.e., solutions that are not globally optimal), the network was trained 100 times for each data partition, each time with different randomly selected starting weights. The best performing network was then selected from these 100 based on the lowest root mean squared error of the network on the reserved test dataset (RMSEtest ). The generalisation ability of a given model setup was assessed by calculating the mean RMSEtest across the ten data partitions. We additionally calculated model performance in terms of mean R2 across the training and test data partitions (R2tr and R2test ) in order to compare to an alternative linear regression approach.

2.5.2. Variable selection Of the ten environmental variables considered, only a subset was selected for the final model in order to avoid inclusion of highly correlated variables, i.e., those having Spearman correlation coefficients (rs ) with an absolute magnitude greater than 0.7 (Dormann et al., 2013). While collinearity is generally considered less problematic for neural networks than for traditional regression analyses, it has been linked to greater errors in the predictive ability of neural networks (e.g. Qin, 1993). Additionally, we found greater instability in the magnitude of neural network connection weights when including highly correlated variables (results not shown). Using the cross-validation procedure outlined above, we selected the combination of variables that provided the best fit to our diversity data (i.e. lowest mean RMSEtest across the ten data partitions). For each variable combination, the optimal size of the hidden layer was first determined by iteratively increasing the hidden layer size from two to ten neurons. While increasing the size of the hidden layer initially decreases both RMSEtest and RMSE of the training data (RMSEtr ), beyond a given number of neurons RMSEtest begins to increase, indicating that overfitting has occurred. The final model was selected taking into account both the model performance (RMSEtest ) and complexity (i.e. number of input variables and number of neurons in the hidden layer). The exclusion of certain variables does not imply that they are unimportant: for example, while we may expect carbon chemistry to affect coccolithophore diversity (Charalampopoulou et al., 2011), the high correlation of calcite saturation state with temperature (r s = 0.91) makes it difficult to separate their effects on a global scale (Table 2). Model performance was better for a network including temperature than for one with calcite saturation state (mean cross-validated RMSEtest ¼ 0:47 as opposed to 0.64; Table 3). Temperature is known to directly affect coccolithophore growth rates (Buitenhuis et al., 2008) and has previously been identified as one of the key drivers of marine diversity (Roy et al., 1998; Rutherford et al., 1999; Worm et al., 2005; Tittensor et al., 2010). We therefore chose to retain temperature as a predictor and excluded calcite saturation state. Phosphate was strongly correlated with all other nutrient variables (nitrate, silicic acid, nitrate gradients and P⁄, r s = 0.74–0.90). While a model including phosphate as the only nutrient variable resulted in a lower RMSE than models including combinations of the other nutrient variables (RMSEtest ¼ 0:46 as opposed to 0.47; Table 3), the optimal hidden layer size for the former model was six neurons, compared to four for other variable combinations. Though the resulting global diversity distribution was similar, an analysis of individual variable response curves from a sensitivity analysis (described in the following section) showed signs of overfitting – i.e. the resulting network was able to model the patterns in the data using phosphate as the only nutrient variable, but only by increasing the complexity of the fitted model through the inclusion of additional neurons in the hidden layer. We therefore excluded phosphate from the final set of variables. Of the remaining eight variables, only nitrate and lateral nitrate gradients were correlated above our cut-off level

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Table 2 Spearman correlation coefficients between all environmental variables considered for inclusion: chlorophyll, temperature, phosphate, nitrate, P⁄, silicate, nitrate gradient, mixed layer depth, calcite saturation state and photosynthetically active radiation. All available global gridcells are included in calculating the correlations. Correlation coefficients shown in bold are those exceeding our threshold of jr s j > 0:7.

Chl SST P N P⁄ Si rN MLD XCa PAR

Chl

SST

P

N

P⁄

Si

rN

MLD

XCa

PAR

1

0.55 1

0.68 0.58 1

0.66 0.68 0.87 1

0.58 0.37 0.90 0.62 1

0.42 0.29 0.74 0.64 0.69 1

0.65 0.67 0.80 0.85 0.64 0.57 1

0.35 0.11 0.09 0.01 0.16 0.06 0.03 1

0.58 0.91 0.62 0.67 0.29 0.71 0.67 0.06 1

0.14 0.43 0.16 0.33 0.05 0.14 0.29 0.27 0.26 1

importance of input variables was estimated using the connection weights approach of Olden et al. (2004). This method calculates the product of each input–hidden and associated hidden–output connection weight (Fig. 1, Table 4). The sum of these connection weight products (hereafter referred to as ‘CW’) is calculated for each variable, with the absolute magnitude used to determine the relative importance of the variable (i.e. the most important variable will have the highest absolute magnitude; Table 4). This approach has previously been shown to be the most robust of the available methods for determining the order of variable importance (Olden et al., 2004). Secondly, we performed a sensitivity analysis to investigate response curves for each input variable. Our sensitivity analysis investigated the effects of changing individual input variables, while all other variables were held at mean conditions for tropical (0–10°), subtropical (10–40°) and temperate (40–60°) waters.

Table 3 Comparison of neural network designs from a 10-fold cross-validation procedure, including the number of input variables (n), the optimal number of neurons in the hidden layer (h), the average root mean square error and coefficient of determination for the 90% data partitions used for training the model (RMSEtr ; R2tr ), and the average root mean square error and coefficient of determination for the 10% data partitions reserved for testing the model (RMSEtest ; R2test ). The selected model is indicated in bold. Input variables ⁄

Chl, SST, N, P , Si, MLD, PAR Chl, SST, P, MLD, PAR Chl, SST, P⁄, rN, Si, MLD, PAR Chl, N, P⁄, MLD, XCa, PAR Chl, P⁄, rN, MLD, XCa, PAR Chl, P, MLD, XCa, PAR

n

h

RMSEtr

RMSEtest

R2tr

R2test

7 5 7 6 6 5

4 6 4 6 6 5

0.46 0.41 0.46 0.55 0.54 0.61

0.47 0.46 0.47 0.64 0.65 0.66

0.79 0.83 0.79 0.70 0.71 0.63

0.78 0.79 0.78 0.59 0.58 0.57

of 0.7 (r s ¼ 0:85). Variable response curves, as investigated in our sensitivity analysis, were more easily separated using nitrate than lateral nitrate gradients, though the same model performance (RMSEtest = 0.47; Table 3) and and similar global diversity patterns were achieved with either variable. Seven variables were therefore included as predictors in the final model: temperature, chlorophyll, nitrate, P⁄, silicic acid, MLD and PAR, with a hidden layer of four neurons (Fig. 1). For the analysis of variable influence, global diversity patterns and future changes, a single representative model was selected from 100 randomly initialised networks (RMSEtest = 0.44; RMSEtr ¼ 0:43).

2.5.4. Global interpolation The resulting model was used to interpolate coccolithophore diversity at the global scale, by applying the trained network to the full range of the seven climatological datasets. We also applied the model to our future climate scenario to determine how coccolithophore diversity may change by the end of the 21st century. 2.5.5. Model evaluation To test whether a non-linear approach was necessary for our dataset, we compared the performance of our neural network model to a linear regression model based on the same input variables. Furthermore, we tested our trained network on different subsets of the data to determine how performance compares across different (a) identification methods (light microscopy versus electron microscopy), (b) regions and (c) seasons.

2.5.3. Analysis of variable influence Two approaches were used to investigate the sensitivity of the model to the different input parameters. Firstly, the order of

Table 4 Analysis of variable influence following the connection weight method (Olden and Jackson, 2002; Olden et al., 2004). The table shows connection weight products for each combination of input-hidden (I–H) and hidden-output (H–O) connection weights (shown in brackets). The summed connection weight products are used to assess the relative influence of each variable in the model, with a higher magnitude implying greater influence.

H1 H2 H3 H4 Sum

Chl

SST

P⁄

N

Si

MLD

PAR

24.57 (2.12, 11.62) 29.90 (2.53, 11.80) 0.93 (0.19, 4.99) 0.62 (0.05, 12.45)

6.03 (0.52, 11.62) 17.59 (1.49, 11.80) 11.81 (2.37, 4.99) 13.97 (1.12, 12.45)

102.93 (8.86, 11.62) 106.14 (9.00, 11.80) 3.05 (0.61, 4.99) 3.37 (0.27, 12.45)

3.18 (0.27, 11.62) 4.36 (0.37, 11.80) 0.62 (0.12, 4.99) 0.39 (0.03, 12.45)

241.63 (20.80, 11.62) 236.82 (20.07, 11.80) 0.95 (0.19, 4.99) 0.28 (0.02, 12.45)

58.13 (5.00, 11.62) 67.59 (5.73, 11.80) 0.48 (0.10, 4.99) 0.86 (0.07, 12.45)

92.48 (7.96, 11.62) 78.94 (6.69, 11.80) 1.01 (0.20, 4.99) 1.52 (0.12, 12.45)

5.64

13.71

2.89

0.95

4.14

9.84

14.05

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3. Results 3.1. Species richness estimates Our final set of gridded diversity estimates consisted of 1092 1
 1
, monthly grid cells, spanning a latitudinal range of 53°S to 89°N. Gridded species richness estimates range from 1 (where only a single species dominated >99% of the total coccolithophore abundance) to 21 species in the most diverse regions in the Indian Ocean. Highest diversity estimates are located in the eastern Indian Ocean and Arabian Sea, with low diversity observed in upwelling regions and the high latitudes (Fig. 3a and b). Low diversity stations tended to be dominated by the species Emiliania huxleyi, while high diversity stations showed a more even distribution of biomass amongst coccolithophore species. Further details of the species composition of coccolithophore assemblages in this dataset will be discussed in a follow-up study. 3.2. Diversity–productivity relationship

25 20

3.3. Modelled diversity patterns Our final model configuration was able to explain 78% of the variance in our coccolithophore diversity dataset, as revealed through our cross-validation procedure (mean R2test ¼ 0:78; mean R2tr ¼ 0:79). The model shows a slight tendency to overestimate diversity at the lower end of our diversity estimates, and underestimate high diversity values (Fig. B.7a). Overall, the model shows a slight positive bias (mean 2.8 ± 1.4 species; Fig. B.7b), but no systematic geographical pattern is seen in the error structure on an annual mean basis (Fig. B.8). Considering the annual mean of our global-scale diversity patterns (Fig. 3c), highest diversity is predicted in the central equatorial Pacific, with additional areas of high diversity expected in the west-Pacific warm pool, the subtropical gyre of the southern Indian Ocean, and at the edges of the Atlantic and Pacific subtropical gyres. Low diversity (an annual mean of 3 or fewer species per assemblage) is predicted in the high latitudes (above approximately 35°). Globally, zonal mean diversity is predicted to be highest at the equator and decline with latitude (Fig. 3d), though this pattern is not consist across all basins. The global pattern is dominated by the Pacific Ocean due to its large surface area, whereas the Atlantic shows a contrasting pattern with diversity peaking in the subtropics at approximately 30°S and 30°N. A strong seasonal cycle is also evident in our modelled diversity fields (Fig. 4), with high diversity bands forming at approximately 30° in each hemisphere during the spring and summer months.

(b) 25 Species Richness

(a) Species Richness

The gridded diversity observations show a negative relationship with total phytoplankton biomass (as inferred from SeaWiFS chlorophyll concentration, Fig. 2b). In contrast, our highest observations of coccolithophore diversity of up to 21 species per grid cell occur at intermediate levels of coccolithophore biomass, with the relationship showing a humped or unimodal form with a diversity maximum at biomass levels of approximately 1 lgC l1 (Fig. 2a). Plotting coccolithophore diversity against our index of coccolithophore dominance (estimated coccolithophore chlorophyll as a percentage of total SeaWiFS chlorophyll for each monthly grid cell) shows a positive relationship between coccolithophore diversity and dominance (Fig. 2c). A quadratic regression fitted to the 95th percentile of our diversity estimates for binned coccolithophore dominance levels shows a plateau only where our coccolithophore

chlorophyll estimate approaches 100% of expected total chlorophyll (Fig. 2c). Highest diversity is therefore occurring either when coccolithophores are a strongly dominant component of the total phytoplankton assemblage, or when total chlorophyll is unseasonably high (i.e. in situ chlorophyll concentrations are greater than climatological chlorophyll values from SeaWIFS).

15 10 5

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Coccolithophore Dominance (% Total Chlorophyll) Fig. 2. Species richness estimates from the MAREDAT coccolithophore dataset (O’Brien et al., 2013, gridded to 1°  1°, monthly values) as a function of (a) coccolithophore biomass (from O’Brien et al., 2013); (b) climatological chlorophyll concentration from SeaWiFS; and (c) coccolithophore dominance (percent contribution of coccolithophore chlorophyll to total chlorophyll). Coccolithophore chlorophyll was estimated from biomass using a conversion factor of 65 g Chl/g C (Sathyendranath et al., 2009). Regression lines are quadratic fits to the 95th percentile of the binned data (excluding outliers with species richness greater than 20).

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Species richness Fig. 3. Global coccolithophore diversity distribution; (a) species richness estimates from the MAREDAT coccolithophore database (annual mean, 1  1 resolution); (b) zonal mean of raw species richness estimates (black) and species richness estimates following rarefaction (red); (c) expected global, annual mean distribution of coccolithophore diversity based on the results of the neural network analysis; (d) zonal mean species richness (global, Atlantic and Pacific) based on results of the neural network analysis.

3.4. Influence of environmental variables Based on the connection weights method (Olden et al., 2004; Table 4), the strongest explanatory variables in the model are PAR (CW = 14.1) and temperature (CW = 13.7). Our sensitivity analysis found an enhancement of diversity at high PAR and high temperature (Fig. 5b and g). For temperature, a secondary diversity peak is seen at approximately 18 °C under temperate and subtropical conditions, and 20 °C under tropical conditions. For the temperate zone, temperature was the only variable that produced any significant change in diversity in our sensitivity analysis, suggesting that this is a key driver of coccolithophore diversity in the high latitudes. The next strongest predictor is MLD (CW = 9.8). The sensitivity analysis suggests that MLD is most important in the tropics, with a strong enhancement in diversity for MLDs between 20 and 30 m. The relationship between chlorophyll and diversity (CW = 5.6) seems to vary depending on the state of other variables. Under tropical conditions we see a unimodal relationship between chlorophyll and diversity, with a maximum at 0.7 mg m3 (Fig. 5a), whereas under subtropical conditions we see a negative relationship between chlorophyll and diversity. Silicic acid (CW = 4.1) shows a unimodal relationship with diversity in the tropics and subtropics (Fig. 5e), with highest diversity associated with silicic acid concentrations of approximately 5–8 lmol l1.

P⁄ is a relatively weak predictor in the final model (CW = 2.9), with the sensitivity analysis showing a clear relationship with diversity only under tropical conditions (Fig. 5c). Maximum diversity is associated with a P⁄ of approximately 0.4 lmol l1. The least important predictor based on the connection weights method is nitrate (CW = 0.95). The sensitivity analysis for this variable shows a weak positive relationship with diversity under tropical and subtropical conditions (Fig. 5d). 3.5. Model evaluation A multiple linear regression model with the same input variables was able to explain only 26% of the variance in our diversity dataset (Table A.5), as opposed to 78% explained by the artificial neural network. This suggests that non-linear effects and interactions are important in determining coccolithophore diversity, and a non-linear approach such as the feed-forward artificial neural network method used here is needed to adequately approximate these relationships. No significant difference in model performance was found when excluding stations where species were identified using electron microscopy (RMSE = 0.44) or a single cruise of high diversity observations from the eastern Indian Ocean (RMSE = 0.44). Thus, there is no significant methodological bias associated with our data.

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Species richness Fig. 4. Global and zonal mean seasonal patterns of coccolithophore diversity as predicted from the neural network: (a–b) December–February; (c–d) March–May; (e–f) June– August; (g–h) September–October.

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PAR (Ein m-2 day-1) Fig. 5. Sensitivity analysis of variable influence: x-axis represent the 5th–95th percentiles of global fields for the seven input variables, y-axis represent diversity predictions from the neural network when all other variables are held constant at mean tropical (0–10, solid line), subtropical (10–40, dashed line) or temperate (40–60, dotted line) conditions. Lines are shown in grey where the variable of interest falls below the 5th percentile or above the 95th percentile of values within the latitudinal range considered.

The input data show a bias towards the summer months (380 grid cells, as opposed to just 144 from winter months, 275 in spring and 278 in autumn). The range of diversity values in winter is low, with just 1–7 species per sample, as opposed to 1–21 species in summer. Nevertheless, model error is lowest for the winter data (RMSE = 0.33) and highest for spring (RMSE = 0.52). 3.6. Coccolithophore diversity projection for 2096–2100 Extrapolating our present day model to projected conditions at the end of the century (2096–2100, Fig. 6) shows strong increases

in diversity in the low latitudes, with zonal, annual mean diversity at the equator increasing from 8 in the present-day model to 14 species by the end of the century (Fig. 6b). Globally, the mean diversity of coccolithophore assemblages is projected to increase to 6.4, compared to present-day diversity estimates of 4.3 species per assemblage. A clear latitudinal gradient is seen in both the diversity change (Fig. 6d) and projected future diversity (Fig. 6b), with greatest increases occurring at the equator and very small or no changes occurring at high latitudes (above approximately 30–40°). The overall trend is an increase in diversity, though slight decreases are seen

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Diversity change Fig. 6. (a) Projected coccolithophore diversity for modelled conditions at the end of the century (2096–2100); (b) zonal mean of projected coccolithophore diversity; (c) projected change in coccolithophore diversity between the periods 1996–2000 and 2096–2100 and (d) zonal mean of projected change in coccolithophore diversity.

in the southern hemisphere between approximately 20–30°S, and in the North Pacific at approximately 30°N (Fig. 6c and d). 4. Discussion 4.1. Global distribution Our results show that a large proportion of the variance in the diversity of coccolithophore assemblages can be explained using environmental predictors – specifically light, temperature, nutrient concentrations, mixed layer depth and total chlorophyll. The two strongest predictors in our model – irradiance and temperature – have previously been shown to be strong drivers of diversity in other marine, aquatic and terrestrial taxa (e.g. Rutherford et al., 1999; Tittensor et al., 2010). Furthermore, many terrestrial taxa show a latitudinal diversity gradient, which some attribute to the greater energy availability in the low latitudes (Clarke and Gaston, 2006). Our extrapolated diversity estimates suggest that at the global scale, annual mean coccolithophore diversity follows a similar latitudinal pattern, with diversity highest at the equator and decreasing toward the poles. However, this latitudinal trend is not consistent between basins: the global pattern is dominated by the Pacific Ocean, which shows a clear latitudinal gradient in zonal mean diversity, whereas in the Atlantic Ocean our results

reproduce the subtropical diversity peaks observed in past studies (Böckel and Baumann, 2008; Cermeño et al., 2008). We believe that the low diversity seen in the tropical Atlantic (Fig. 3c and d) is linked to the influence of the highly productive Benguela and Canary current upwelling systems to the east and the Amazon outflow to the west. These regions are characterised by relatively high chlorophyll concentrations due to the upwelling of nutrients – high chlorophyll concentrations were found to have a negative effect on coccolithophore diversity in our model. The influence of chlorophyll concentrations on our diversity estimates can also be seen in the equatorial Pacific, with very low diversity (>5 species per assemblage) predicted in the vicinity of the Peru– Chile upwelling system to the east, and up to 20 species per assemblage predicted to co-occur in the lower productivity western and central regions (Fig. 3c). In addition to these regional- and basin-scale differences, our diversity patterns show strong seasonality in the subtropics, with diversity increasing at approximately 30°N and S in the spring and summer months (Fig. 4). This seasonal cycle is consistent with observations of coccolithophore diversity by Winter et al. (1979), and a similar pattern is seen for total phytoplankton diversity in a recent modelling study (Vallina et al., 2014a). The latitudinal gradient seen in annual mean diversity is therefore a composite of overlapping seasonal diversity patterns, but will not be present at any single point in time.

C.J. O’Brien et al. / Progress in Oceanography 140 (2016) 27–42

4.2. Environmental drivers We expected the strongest explanatory variables to be those that have been linked to higher diversity across taxa, for example higher energy input (i.e. PAR, temperature), greater resource availability (i.e. higher nutrient concentrations) or increased horizontal mixing of phytoplankton assemblages (indicated by higher lateral nitrate gradients). While we did find the expected positive correlation of coccolithophore diversity with temperature and PAR, further investigation showed that our environmental predictors rather reflect conditions which are favourable for coccolithophore growth and/or which give coccolithophores a competitive advantage over other phytoplankton taxa (e.g. low silicic acid concentrations and low to intermediate chlorophyll concentrations). This implies that the diversity patterns seen here are likely to be specific to coccolithophores rather than reflecting general relationships between environmental conditions and diversity across taxa. The link between coccolithophore diversity and ecological processes such as competition is most apparent with respect to the effects of nutrients and chlorophyll. Whereas previous studies have suggested a link between diversity and the supply of essential resources (Gross and Cardinale, 2007), we found that silicic acid and P⁄ were the strongest nutrient-related predictors, despite the fact that silicic acid is not required for coccolithophore growth and P⁄ reflects the ratio rather than absolute availability of key macronutrients. Similarly, diversity shows a negative relationship with chlorophyll in our model and for the gridded diversity estimates. A unimodal relationship is seen for the tropical regions in our model, though with an optimum at a relatively low concentration of approximately 0.7 mg m3. We expect that these variables are acting as a proxy for competition: high coccolithophore diversity occurs in regions where silicic acid is depleted and total phytoplankton biomass is low, since these are regions where they are not outcompeted by diatoms and other r-selected phytoplankton. While coccolithophores are still reported at higher nutrient and chlorophyll concentrations, the species that occur in these conditions are only a small number of more r-selected coccolithophores, for example the blooming species E. huxleyi (Brun et al., 2015; Charalampopoulou et al., 2011; Okada and McIntyre, 1977). At extremely low nutrient concentrations, we see a decline in the number of coccolithophore species – we expect that this is due to the coccolithophores becoming nutrient-limited and being increasingly replaced by phytoplankton with even lower nutrient requirements, for example picophytoplankton and nitrogen-fixing cyanobacteria. The importance of P⁄ as a predictor, particularly in the tropical regions, is interesting given the conflicting relationships that have been found between coccolithophore occurrence and nutrient ratios. Previous studies have found associations between high E. huxleyi abundance and low N:P ratios (Van der Wal et al., 1995; Maranon and Gonzalez, 1997), though conversely E. huxleyi blooms have also been linked to high N:P ratios (Tyrrell and Taylor, 1996), and Zondervan (2007) suggested that N:P ratios are not a good predictor of bloom occurrence. We are not aware of studies investigating the importance of N:P ratios for other coccolithophore species or for coccolithophore diversity, though our results suggest that further investigations may be warranted, particularly in the low latitudes. The relatively low importance of nitrate and phosphate as predictors of coccolithophore diversity in our model may be explained by the imperfect correlation between the nutrient concentration data we use here and nutrient supply rates, which we expect to have a strong control on phytoplankton ecology (Dutkiewicz et al., 2014). Unfortunately, no reliable, global-scale datasets exist for nutrient supply rates, and the high uncertainties associated

37

with model- or observation-based estimates limit their value as model predictors. The association of high coccolithophore diversity with high irradiance and temperature is also consistent with current knowledge of coccolithophore ecology and physiology. The enhancement of diversity at high irradiance is likely due to the ability of coccolithophores to survive and grow in high light conditions. While light saturation curves have only been studied for few species, coccolithophore growth seems to saturate at higher light intensities compared to diatoms and dinoflagellates (Zondervan, 2007; Heinle, 2014), thus giving them a competitive advantage in high light conditions. For temperature, the first diversity peak identified in the sensitivity analysis between 18 and 20 °C corresponding to the optimal growth temperature for coccolithophores (Buitenhuis et al., 2008; Heinle, 2014). The further increase in diversity above 27 °C may be due to an increase in the competitive ability of coccolithophores compared to other phytoplankton groups – i.e., while coccolithophore growth rates are somewhat lower at these temperatures, they may not be affected as strongly as other groups such as diatoms. The importance of temperature in limiting coccolithophore species distributions is further supported by the recent range extensions of coccolithophores in the high latitudes in response to ocean warming (Merico et al., 2003; Winter et al., 2013). Finally, MLD was found to have a strong influence in the tropics, with a sharp increase in diversity at MLDs between 20 and 30 m. We expect that an MLD shallower than 20 m becomes disadvantageous to coccolithophore diversity due to a combination of increasing nutrient limitation and a decrease in vertical niche partitioning within the mixed layer. Our global diversity distribution is consistent with current knowledge of coccolithophore species distributions. The majority of coccolithophore species have previously been found to be Kselected, occurring in the low-nutrient, stable oligotrophic regions of the oceans (Brand, 1994), with only few species (including the blooming species E. huxleyi) occurring in the more temporally variable and higher nutrient high latitude regions (Brun et al., 2015; Charalampopoulou et al., 2011; Okada and McIntyre, 1977). In Margalef’s mandala (Margalef, 1978; Balch, 2004), the coccolithophores inhabit an intermediate position between the r-selected diatoms and the more K-selected dinoflagellates, a finding that has been supported by subsequent analyses (e.g. Brun et al., 2015). Similarly, our results show that coccolithophore diversity peaks in intermediate productivity waters, supporting our hypothesis that the coccolithophore diversity distribution is related to the realised ecological niche of this group – i.e. the combined effects of environmental conditions on coccolithophore growth and competition with other phytoplankton taxa determine the diversity of coccolithophore assemblages.

4.3. Diversity–productivity relationship Our species richness and biomass estimates suggest that highest coccolithophore diversity is associated with intermediate levels of coccolithophore biomass (which we assume is a reasonable proxy for coccolithophore productivity; Fig. 2a). A similar humpshaped relationship has previously been shown for total phytoplankton assemblages (Irigoien et al., 2004), as well as for numerous terrestrial and aquatic taxa (Waide et al., 1999). In contrast, we see a negative relationship between coccolithophore diversity and chlorophyll (Fig. 4b), which we use as a proxy for total primary productivity. We expect that this relationship reflects the role of competition with other phytoplankton groups, with fewer coccolithophore species able to successfully

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compete in higher productivity regions where faster-growing species such as diatoms become more prevalent. Our results also suggest that diversity increases with increasing coccolithophore dominance of the phytoplankton assemblage. Highest dominance and diversity occur in regions of low total primary productivity (e.g. the eastern Indian Ocean and subtropical gyres). Although absolute coccolithophore biomass in these regions is lower than in the temperate regions, where E. huxleyi forms extensive blooms (Holligan et al., 1993), the relative contribution of coccolithophores to total phytoplankton biomass is much greater – our estimates of coccolithophore chlorophyll are as high as total expected chlorophyll for some stations. We expect that some of the highest dominance estimates may be explained by high productivity events, with total chlorophyll reaching levels greater than expected based on the climatological data. A similar relationship between dominance and diversity seems to exist for diatoms in the Atlantic Ocean (Cermeño et al., 2008), with highest diatom diversity observed in temperate and upwelling regions where diatoms also tend to dominate total phytoplankton biomass. These findings are consistent with a recent modelling study by Vallina et al. (2014a), which predicted different productivity–diversity relationships depending on the nutrient uptake capabilities of the functional group considered. Functional groups with low nutrient affinity showed highest diversity in regions of low primary productivity, whereas faster-growing groups such as diatoms showed highest diversity under conditions of high primary productivity. Likewise, we show that the relatively low to intermediate nutrient affinity coccolithophores reach highest diversity in lowto intermediate-productivity waters such as the western equatorial Pacific. We hypothesise that diversity of other marine phytoplankton taxa will be highest in different ocean regions depending on the ecological niche of a particular group, rather than in cross-taxa diversity hotspots. Any consistent relationship between diversity and productivity is therefore likely to be seen only within taxonomic or functional groups, while different groups will show varying relationships between diversity and total primary productivity. 4.4. Comparison to previous studies The diversity patterns revealed in our gridded diversity observations are largely consistent with past studies of coccolithophore diversity, with highest values occurring in the tropics and subtropics and low values in the high latitudes and upwelling regions. While we are lacking abundance observations for certain regions, some studies investigating the diversity and species composition of coccolithophores are available and can be used as independent datasets with which to evaluate our results. For example, our prediction of up to 21 species per station in the subtropical Pacific is consistent with observations from Honjo and Okada (1974), who recorded a diversity peak of up to 25 species per sample at approximately 30°N to 35°N during a cruise in the central Pacific. Hagino et al. (2000) recorded even higher diversity in the western equatorial Pacific, with up to 40 species per station. North of 38°N, Honjo and Okada noted that coccolithophore assemblages were composed almost entirely of E. huxleyi, supporting our hypothesis of very low diversity at these latitudes. On the other hand, they recorded only 11 species per sample at the equator, lower than our estimates at a comparable longitude (150°W) of up to 20 species. However, our modelled diversity patterns in the equatorial Pacific show a clear shift from very low diversity in the nutrientrich waters upwelled off the Peruvian coast to high diversity in the warmer, more highly stratified and nutrient-depleted waters to the west. The transect investigated by Honjo and Okada falls more or less at the boundary between our predicted high diversity

region in the western equatorial Pacific and the lower diversity region to the east (Fig. 3d). We expect considerable interannual variability to occur in this region due to the effects of the El Nino Southern Oscillation (ENSO) climate mode, in addition to the seasonal variation which is included in our model. 4.5. Future coccolithophore diversity Our future projections for coccolithophore diversity show greatest increases in the low latitudes, primarily due to the positive effect of increasing temperatures. We expect that future climate change may lead to a poleward range expansion for many coccolithophore species, as has already been observed in the high latitudes (Beaugrand et al., 2002, 2010; Reid et al., 2007). Our results are consistent with past studies of the likely impact of climate change on coccolithophores, which predicted decreases in E. huxleyi bloom occurrence in the temperate regions (IglesiasRodríguez et al., 2002), but an increase in coccolithophore biomass in the low latitudes (Tyrrell et al., 2007; Cermeño et al., 2008), and thus a likely potential overall reduction in global coccolithophore biomass. A major uncertainty associated with these projections is the potential impact of ocean acidification. Although temperature and saturation state are at present highly correlated at the global and monthly scale (Table 2), the combined effects of climate change and ocean acidification are likely to change the form of this relationship, with a general trend towards warmer waters but lower saturation state (Doney et al., 2009). Although we found temperature to be a stronger predictor than calcite saturation state, this may be at least partially due to the poorer resolution of the calcite dataset (4
 5
, as opposed to 1
 1
for the temperature data). The calcite saturation state data also has a higher level of uncertainty due to the propagation of errors from the variables used to calculate it (carbon chemistry, temperature and nutrient concentrations). Thus, we suggest that further investigations are still required to determine whether calcite saturation state can influence coccolithophore diversity. Comparing our diversity distribution to global calcite production patterns (Balch et al., 2007), we see that highest calcification rates occur in regions of relatively low coccolithophore diversity, for example the North Atlantic and the ‘great calcite belt’ in the Southern Ocean. The contribution of relatively few coccolithophore species to calcite production in the high latitudes highlights the need to focus research into the impacts of climate change and ocean acidification on key coccolithophore species in the temperate regions, in particular the blooming species E. huxleyi (e.g. Schlüter et al., 2014). However, the contribution of coccolithophores in the higher diversity tropics and subtropics to global biomass and calcite production are currently less well constrained, and deserving of further investigation. We nevertheless expect the high diversity of coccolithophores in the low latitudes to correspond to a high level of niche complementarity and/or redundancy, with coccolithophore assemblages in these regions likely to be more resilient to future environmental change. We recommend further investigation of the relationship between coccolithophore diversity and biomass in order to further understand the implications of these results for coccolithophore biomass and calcification. Comparing our diversity distribution to global calcite production estimates (Balch et al., 2007), we see that highest calcification rates occur in regions of relatively low coccolithophore diversity, for example the North Atlantic and the ‘great calcite belt’ in the Southern Ocean. The contribution of relatively few coccolithophore species to calcite production in these regions highlights the need to focus research into the impacts of climate change and ocean acidification on key coccolithophore species in the temperate regions, in particular the blooming species E. huxleyi (e.g. Schlüter et al., 2014).

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Predicted Species Richness

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Model Error (Model - Data) Fig. B.7. (a) Modelled species richness (y-axis) versus species richness observations (x-axis). A linear regression fit between the modelled and observed diversity values is shown in red, the dashed line indicates a 1:1 fit. (b) Frequency histogram of model bias (modelled species richness–gridded species richness observations for all monthly grid cells where data is available. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

In contrast, we expect the high diversity of coccolithophores in the low latitudes to correspond to a high level of niche complementarity and/or redundancy, with coccolithophore assemblages in these regions likely to be more resilient to future environmental change. The contribution of coccolithophore assemblages in the higher diversity tropics and subtropics to global biomass and calcite production is, however, currently unknown. We therefore recommend further investigation of the contribution of these highly diverse non-bloom coccolithophore assemblages to global coccolithophore biomass in order to improving our understanding of the relationship between the diversity and biomass of coccolithophore assemblages and their sensitivity to environmental change. Such studies would also greatly improve estimates of the relative contribution of coccolithophores to global phytoplankton biomass and the relative importance of coccolithophore calcification compared to calcifying zooplankton (i.e. pteropods and foraminifera). 4.6. Caveats Several sources of uncertainty are associated with our results. Firstly, the raw diversity estimates themselves are associated with

considerable uncertainty due to the varying quality of the observational datasets. For a comparison of sampling effort across datasets, the volume and/or number of cells analysed for each sample would be valuable additional information rather than relying solely on cell densities. There is also a general bias in the dataset towards regions with relatively low coccolithophore diversity such as coastal and upwelling regions – we expect that the addition of further data from the low latitudes would increase the proportion of higher diversity stations in the dataset. Inclusion of further data points from the equatorial Pacific are particularly needed to improve the robustness of our results, given that this is the region where highest diversity is predicted. Furthermore, the use of rarefaction was necessary in this study to control for the varying sampling completeness of different datasets. This approach effectively excludes species with a relative abundance of less than approximately 1% from our species richness estimates. While we argue that the diversity of the more dominant species is likely to be more relevant to ecosystem functioning, additional studies of coccolithophore diversity which combine the advantages of SEM and light microscopy (and potentially also genetic techniques) would enable us to better understand the importance of rare species to ecosystem functioning. Secondly, for our investigation of the productivity–diversity relationship we used chlorophyll concentration as a proxy for total primary productivity. We recognise, however, that considerable variability is known to occur in carbon to chlorophyll ratios as well as primary productivity per unit chlorophyll in response to factors such as light, nutrient availability and community composition. Therefore, while we found a consistent relationship between chlorophyll and diversity and primary productivity and diversity for our dataset, this relationship may not hold at the global scale. Finally, while this study has shown that a large proportion of the global variance in coccolithophore diversity, we expect that our model is missing some important processes which influence the diversity of coccolithophore assemblages. Grazing, for example, has been proposed as an important control of phytoplankton diversity (Prowe et al., 2012; Vallina et al., 2014a,b). While we were unable to investigate the potential contribution of top-down controls due to a lack of global-scale datasets for variables such as zooplankton grazing rates, we speculate that some variables included in our model can also be considered proxies for grazing control, for example chlorophyll concentration, temperature and mixed layer depth, allowing us to reproduce diversity patterns without explicitly including top-down controls. Disturbance frequency has also been shown to affect diversity, with highest diversity usually associated with intermediate levels of disturbance that prevent the system from reaching its equilibrium state (Connell, 1978). In marine systems, oceanographic

Table A.5 Best multiple linear regression model achieved through a stepwise procedure using the same 10 variables considered for neural network development. Final variables selected for inclusion in the regression model were chlorophyll, temperature, P⁄, lateral nitrate gradient, photosynthetically active radiation. Values shown are the regression coefficients (‘Coeff’) and their standard error (‘SE’), t-statistic (t) and pvalue (p). Model R2 ¼ 0:26.

(Intercept) CHL SST P⁄ rN PAR

Coeff

SE

t

p

0.19 0.13 0.14 0.13 0.17 0.12 0.08

12.98 9.88 2.39 3.78 3.04 5.14 5.89

<0.001 <0.001 0.02 <0.001 <0.01 <0.001 <0.001

CHL2

2.45 1.32 0.34 0.49 0.51 0.64 0.49

rN2

0.35

0.11

3.29

<0.01

PAR2

0.24

0.10

2.41

0.02

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80° E

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Model − Data Fig. B.8. Annual mean model bias (modelled diversity–gridded diversity observations for all grid cells where data is available).

features such as frontal regions and eddies have been linked to enhanced diversity not only through the physical disturbance they cause, but also through the advection and mixing of plankton from different water masses (Barton et al., 2010; D’Ovidio et al., 2010; Chust et al., 2013; Clayton et al., 2013). While we attempted to include some level of environmental disturbance in our model through the consideration of lateral nitrate gradients as a possible explanatory variable, this variable did not provide any additional explanatory power compared to using the raw nutrient concentrations. Smaller-scale temporal and spatial variability is unable to be captured at the resolution addressed in our study.

5. Conclusions Our results suggest that temperature and light are key drivers of diversity patterns in the coccolithophores, and are largely responsible for the latitudinal diversity patterns we see for this group. Our findings are consistent with the view of coccolithophores as a group of predominantly K-selected species, which are ecologically dominant in transitional regions between the high productivity temperate regions and the oligotrophic gyres. While our model suggests a future increase in the diversity of coccolithophore assemblages, this response is likely to be further complicated by the additional effects of ocean acidification. Further analysis of diversity patterns within other phytoplankton groups as well as for total phytoplankton assemblages are still needed to fully understand the importance of diversity to productivity, biomass and ecosystem function in marine planktonic ecosystems.

Acknowledgements We thank Debora Iglesias-Rodriguez and two anonymous reviewers for their valuable comments on an earlier version of the manuscript. The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7 2007-2013) under grant agreement no. 238366.

Appendix A. Multiple linear regression model See Table A.5.

Appendix B. ANN performance See Figs. B.7 and B.8.

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